618 citations · 861 across the 14 of their papers we have counts for
14 papers
Fast Lossless Neural Compression with Integer-Only Discrete Flows
Siyu Wang, Jianfei Chen, Chongxuan Li +2
By applying entropy codecs with learned data distributions, neural compressors have significantly outperformed traditional codecs in terms of compression ratio. However, the high i…
Estimating the Optimal Covariance with Imperfect Mean in Diffusion Probabilistic Models
Fan Bao, Chongxuan Li, Jiacheng Sun +2
Diffusion probabilistic models (DPMs) are a class of powerful deep generative models (DGMs). Despite their success, the iterative generation process over the full timesteps is much…
Analytic-DPM: an Analytic Estimate of the Optimal Reverse Variance in Diffusion Probabilistic Models
Fan Bao, Chongxuan Li, Jun Zhu +1
Diffusion probabilistic models (DPMs) represent a class of powerful generative models. Despite their success, the inference of DPMs is expensive since it generally needs to iterate…
Twins: Revisiting the Design of Spatial Attention in Vision Transformers
Xiangxiang Chu, Zhi Tian, Yuqing Wang +5
Very recently, a variety of vision transformer architectures for dense prediction tasks have been proposed and they show that the design of spatial attention is critical to their s…
Bi-level Score Matching for Learning Energy-based Latent Variable Models
Fan Bao, Chongxuan Li, Kun Xu +3
Score matching (SM) provides a compelling approach to learn energy-based models (EBMs) by avoiding the calculation of partition function. However, it remains largely open to learn…
Variational (Gradient) Estimate of the Score Function in Energy-based Latent Variable Models
Fan Bao, Kun Xu, Chongxuan Li +3
The learning and evaluation of energy-based latent variable models (EBLVMs) without any structural assumptions are highly challenging, because the true posteriors and the partition…
Multi-Objective Reinforced Evolution in Mobile Neural Architecture Search
Xiangxiang Chu, Bo Zhang, Ruijun Xu +1
Fabricating neural models for a wide range of mobile devices demands for a specific design of networks due to highly constrained resources. Both evolution algorithms (EA) and reinf…
Deep Structured Generative Models
Kun Xu, Haoyu Liang, Jun Zhu +2
Deep generative models have shown promising results in generating realistic images, but it is still non-trivial to generate images with complicated structures. The main reason is t…
Learning Implicit Generative Models by Teaching Explicit Ones
Chao Du, Kun Xu, Chongxuan Li +2
Implicit generative models are difficult to train as no explicit density functions are defined. Generative adversarial nets (GANs) present a minimax framework to train such models,…
Fast Deep Matting for Portrait Animation on Mobile Phone
Bingke Zhu, Yingying Chen, Jinqiao Wang +3
Image matting plays an important role in image and video editing. However, the formulation of image matting is inherently ill-posed. Traditional methods usually employ interaction…
Bootstrapping Face Detection with Hard Negative Examples
Shaohua Wan, Zhijun Chen, Tao Zhang +2
Recently significant performance improvement in face detection was made possible by deeply trained convolutional networks. In this report, a novel approach for training state-of-th…
Learning to Generate with Memory
Chongxuan Li, Jun Zhu, Bo Zhang
Memory units have been widely used to enrich the capabilities of deep networks on capturing long-term dependencies in reasoning and prediction tasks, but little investigation exist…
Learning Deep Generative Models with Doubly Stochastic MCMC
Chao Du, Jun Zhu, Bo Zhang
We present doubly stochastic gradient MCMC, a simple and generic method for (approximate) Bayesian inference of deep generative models (DGMs) in a collapsed continuous parameter sp…
Max-margin Deep Generative Models
Chongxuan Li, Jun Zhu, Tianlin Shi +1
Deep generative models (DGMs) are effective on learning multilayered representations of complex data and performing inference of input data by exploring the generative ability. How…